syne_tune.optimizer.schedulers.neuralbands.neuralband module
- syne_tune.optimizer.schedulers.neuralbands.neuralband.is_continue_decision(trial_decision)[source]
- Return type:
bool
- class syne_tune.optimizer.schedulers.neuralbands.neuralband.NeuralbandScheduler(config_space, gamma=0.01, nu=0.01, step_size=30, max_while_loop=100, **kwargs)[source]
Bases:
NeuralbandSchedulerBase
NeuralBand is a neural-bandit based HPO algorithm for the multi-fidelity setting. It uses a budget-aware neural network together with a feedback perturbation to efficiently explore the input space across fidelities. NeuralBand uses a novel configuration selection criterion to actively choose the configuration in each trial and incrementally exploits the knowledge of every past trial.
- Parameters:
config_space (
Dict
) –gamma (
float
) – Control aggressiveness of configuration selection criterionnu (
float
) – Control aggressiveness of perturbing feedback for explorationstep_size (
int
) – How many trials we train network oncemax_while_loop (
int
) – Maximal number of times we can draw a configuration from configuration spacekwargs –